A meta-analysis of global stillbirth rates during the COVID-19 pandemic.
Bibliographic record
Abstract
Background The global effect of the COVID-19 pandemic has had an impact on pregnancy and outcomes. There has been recently some conflicting evidence on the stillbirths during the COVID-19 pandemic. This meta-analysis attempts to resolve this through a systematic approach. Objectives To analyse and determine the impact of COVID-19 on the stillbirth rate. Search strategy We searched PubMed, Embase, Cochrane library, ClinicalTrials.gov and Web of Science from inception to 05 March 2021 with no language restriction for this meta-analysis. Selection criteria Publications (a) with stillbirth data on pregnant women with COVID-19 (b) comparing stillbirth rates in pregnant women with and without COVID-19 and (c), comparing stillbirth rates before and during the pandemic. Data collection and Analysis The included studies were all observational studies, and we used the Newcastle Ottawa score for risk of bias. We performed the meta-analysis using Comprehensive meta-analysis software, version 3. Main results A total of 29 studies were included in the meta-analysis; from 17 of these, the SB rate was 7 per 1000 in pregnant women with COVID-19. This rate was much higher (34/1000) in low- and middle-income countries. The odds ratio of stillbirth in pregnant women with COVID-19 compared to those without was 1.89. However, there was no significant difference in population SB rates before and during the pandemic. Conclusions There is some evidence that the stillbirth rate has increased during the COVID-19 pandemic, but this is mainly in low- and middle-income countries. Inadequate access to healthcare during the pandemic could be a contributing factor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.082 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".